Potential crop yield loss due to early-season weed competition is an important risk associated with postemergence weed management programs. WeedSOFT is a weed management decision support system that has the potential to greatly reduce such risk. Previous research has shown that weed emergence time can greatly affect the accuracy of corn yield loss predictions by WeedSOFT, but our understanding of its predictive accuracy for soybean yield loss as affected by weed emergence time is limited. We conducted experiments at several sites across the Midwestern United States to assess accuracy of WeedSOFT predictions of soybean yield loss associated with mixed-species weed communities established at emergence (VE), cotyledon (VC), first-node (V1), or third-node (V3) soybean. Weed communities across research sites consisted mostly of annual grass species and moderately competitive annual broadleaf species. Soybean yield loss occurred in seven of nine site-years for weed communities established at VE soybean, four site-years for weed communities established at VC soybean, and one site-year for weed communities established at V1 soybean. No soybean yield loss was associated with weed communities established at the V3 stage. Nonlinear regression analyses of predicted and observed soybean yield data pooled over site-years showed that predicted yields were less than observed yields at all soybean growth stages, indicating overestimation of soybean yield loss. Pearson correlation analyses indicated that yield loss functions overestimated the competitive ability of high densities of giant and yellow foxtail with soybean, indicating that adjustments to competitive index values or yield loss function parameters for these species may improve soybean yield loss prediction accuracy and increase the usefulness of WeedSOFT as a weed management decision support system.

Crop seedlings detect the presence of neighboring competitors by means of the red to far-red ratio (R/FR) of light reflected from the leaf surfaces of adjacent seedlings. Although previous studies have suggested that shifts in the R/FR initiate crop–weed competition, no studies have documented the R/FR of light reflected from weeds or explored how weed management practices may affect the R/FR. Experiments were conducted to test the following hypotheses: (1) the duration of R/FR signals reflected from the leaf surface of weed seedlings will vary among herbicides following treatment and will decline faster as the dose of a given herbicide increases, (2) the R/FR of reflected light will differ among weed species, and (3) the R/FR of reflected light will decrease as weed seedling leaf area and stage of development increases. Velvetleaf was used as a model weed species to examine herbicide chemistry and dose, and six weed species including Powell amaranth, velvetleaf, Eastern black nightshade, barnyardgrass, proso millet, and green foxtail were evaluated in order to characterize the R/FR of light reflected from their leaf surfaces. Results of this study confirm that the R/FR reflected from the leaf surface of weeds is affected by: herbicide chemistry, herbicide dose, weed species, stage of weed development, and distance of the weed from the crop. The relative decline in the R/FR (as a percent of the untreated control) was most rapid following treatment with paraquat, followed by glufosinate and then glyphosate. As glyphosate dose decreased, so did the reduction in the relative R/FR. Based on reflected R/FR, weed species tended to be grouped into monocots and dicots, with the latter reflecting a lower R/FR per unit leaf area than the former. This disparity was attributed to the compact leaf arrangement and orientation of dicot weed canopies, which may contribute to the greater competitiveness of dicot weeds.

Assessing belowground plant interference in rice has been difficult in the past because intertwined weed and crop roots cannot be readily separated. A 13C discrimination method has been developed to assess distribution of intermixed roots of barnyardgrass and rice in field soils, but the suitability of this approach for other rice weeds is not known. 13C depletion levels in roots and leaves of rice were compared with those of 10 troublesome weed species grown in monoculture in the greenhouse or field. Included were C4 tropical grasses: barnyardgrass, bearded sprangletop, Amazon sprangletop, broadleaf signalgrass, fall panicum, and large crabgrass; C4 sedge, yellow nutsedge; and C3 species: red rice, gooseweed, and redstem. Rice root δ13C levels averaged ∼ −28‰, indicating that these roots are highly 13C-depleted. Root δ13C levels ranged from −12‰ to −17‰ among the tropical grasses, and were −10‰ in yellow nutsedge, indicating that these species were less 13C depleted than rice, and were C4 plants suitable for 13C discrimination studies with rice. Among the C4 species, bearded sprangletop and yellow nutsedge were most and least 13C depleted, respectively. δ13C levels in shoot and root tissue of pot-grown plants averaged 6% greater for C4 plants and 9% greater for rice in the field than in the greenhouse. In pots, shoots of rice typically were slightly more 13C depleted than roots. A reverse trend was seen in most C4 species, particularly for broadleaf signalgrass and plants sampled from field plots. Corrections derived from inputs including the total mass, carbon mass, carbon fraction, and δ13C levels of roots and soil increased greatly the accuracy of root mass estimates and increased slightly the accuracy of root δ13C estimates (∼ 0.6 to 0.9%) in samples containing soil. Similar corrective equations were derived for mixtures of rice and C4 weed roots and soil, and are proposed as a labor-saving option in 13C discrimination root studies.

Although foliar herbicide absorption has been studied intensively, there is currently no standardized method for data analysis when evaluating herbicide absorption over time. Most peer-reviewed journals require the treatment structure of data be incorporated in the analysis; however, many herbicide absorption studies published in the past 5 yr do not account for the time structure of the experiment. Herbicide absorption studies have been presented in a variety of ways, making it difficult to compare results among studies. The objective of this article is to propose possible nonlinear models to analyze herbicide absorption data and to provide a stepwise framework so that researchers may standardize the analysis method in this important research area. Asymptotic regression and rectangular hyperbolic models with similar parameterizations are proposed, so that the maximum herbicide absorption and absorption rate may be adequately modeled and statistically compared among treatments. Adoption of these models for herbicide absorption analysis over time will provide a standardized method making comparison of results within and among studies more practical.

Mayweed chamomile seeds were collected from six different fields across the Pacific Northwest. All populations (each collection site was considered a population) were suspected to have some level of acetolactate synthase (ALS) resistance. Greenhouse and laboratory studies were conducted to determine if these populations were resistant to three different classes of ALS inhibitors: sulfonylureas (SU), imidazolinones (IMI), and triazolopyrimidines (TP). A whole-plant dose–response and in vitro ALS activity studies confirmed cross-resistance to thifensulfuron + tribenuron/chlorsulfuron (SU), imazethapyr (IMI), and cloransulam (TP); however, resistance varied by herbicide class and population. Two ALS isoforms of the ALS gene (ALS1 and ALS2) were identified in mayweed chamomile; however, only mutations in ALS1 were responsible for resistance. No mutations were found in ALS2. Sequence analysis of the partial ALS gene identified four point mutations at position 197 (Pro197 to Leu, Gln, Thr, or Ser) in the resistant populations. This study demonstrates genotypic variation associated with cross-resistance to ALS inhibitors within and between populations.

Growing enough cover crop biomass to adequately suppress weeds is one of the primary challenges in reduced-tillage systems that rely on mulch-based weed suppression. We investigated two approaches to increasing cereal rye biomass for improved weed suppression: (1) increasing soil fertility and (2) increasing cereal rye seeding rate. We conducted a factorial experiment with three poultry litter application rates (0, 80, and 160 kg N ha−1) and three rye seeding rates (90, 150, and 210 kg seed ha−1) in Pennsylvania and Maryland in 2008 and 2009. We quantified rye biomass immediately after mechanically terminating it with a roller and weed biomass at 10 wk after termination (WAT). Rye biomass increased with poultry litter applications (675, 768, and 787 g m−2 in the 0, 80, and 160 kg N ha−1 treatments, respectively), but this increased rye biomass did not decrease weed biomass. In contrast, increasing rye seeding rate did not increase rye biomass, but it did reduce weed biomass (328, 279, and 225 g m−2 in the 90, 150, and 210 kg seed ha−1 treatments, respectively). In 2009, we also sampled ground cover before rolling and weed biomass and density at 4 WAT. Despite no treatment effects, we found a correlation between bare soil before rolling (%) and weed biomass at 4 WAT. Our results suggest that increased rye seeding rate can effectively reduce weed biomass and that ground cover in early spring can influence weed biomass later in the growing season.